Methods for producing new packaging designs based on photoprotective materials

ABSTRACT

Methods and apparatus for quantification of photoprotective performance of packaging concepts in an accelerated timeframe are disclosed. In certain embodiments, the apparatus includes a light source which provides a light beam that impinges upon a photoprotective material before being transmitted to a sample cell of a photosensitive entity, such as a photosensitive nutrient. In certain embodiments, the apparatus and methods can be used to generate models for the prediction of photoprotective performance values of untested materials based upon some other known qualitative or quantitative property.

This case claims priority back and is related to Application No.61/639,309 having a filing date of Apr. 27, 2012. Application No.61/639,309 is hereby incorporated by reference.

FIELD OF THE INVENTION

This invention relates to the field of packaging technology, morespecifically to methods and apparatus for testing materials forphotoprotective properties.

BACKGROUND

It is well understood that certain compounds and nutrients containedwithin packages can be negatively impacted by exposure to light. Manydifferent chemical and physical changes can result to molecular speciesas either a direct or indirect result of exposure to light, which cancollectively be defined as photochemical processes. As described inAtkins, photochemical processes can include primary absorption, physicalprocesses (e.g., fluorescence, collision-induced emission, stimulatedemission, intersystem crossing, phosphorescence, internal conversion,singlet electronic energy transfer, energy pooling, triplet electronicenergy transfer, triplet-triplet absorption), ionization (e.g., Penningionization, dissociative ionization, collisional ionization, associativeionization), or chemical processes (e.g., disassociation or degradation,addition or insertion, abstraction or fragmentation, isomerization,dissociative excitation) (Atkins, P. W.; Table 26.1 PhotochemicalProcesses. Physical Chemistry, 5^(th) Edition; Freeman: New York, 1994;908). As one example, light can cause excitation of photosensitizerspecies (e.g., riboflavin in dairy food products) that can thensubsequently react with other species present (e.g., oxygen, lipids) toinduce changes, including degradation of valuable products (e.g.,nutrients in food products) and evolution of species that can adjust thequality of the product (e.g., off-odors in food products).

Protection of food, including its sensory quality and nutritional value,are of essential importance to society to protect our food supply andminimize food wastage thus ensuring sustainability in our food supplyand distribution. Here sustainability considerations primarily include,but are not limited to, the cost, the use of non-renewable and renewableresources, environmental impacts, and water usage. The sustainabilityimpact of food extends to include its production, packaging,distribution, usage, and waste accumulated through these processes.Furthermore, with the growing global population, sustainability also isrelated to food security and the nutritional value delivered by food.

As such, there is an art-recognized need to provide packaging withsufficient photoprotective properties to allow the protection of thepackage content(s). In certain studies, actual packaging systems andphotochemical reactors have been used as a means to provide anindication of photoprotective performance of packaging concepts.However, generally these studies allow for the evaluation of only asingle packaging concept and do not demonstrate sufficiently robustmethods to allow for relative comparisons between experiments, nor thecapability to produce performance design models based on the results.

For instance, the work of Kline et al. (Kline, M. A.; Duncan, S. E.;Bianchi, L. M.; Eigel, W. N., III; O'Keefe, S. F.; Light WavelengthEffects on a Lutein-Fortified Model Colloidal Beverage. J. Agric, FoodChem. 2011, 59, 7203-7210) studying the light effects on a modelcolloidal beverage acknowledges the challenge to make relativecomparisons between experimental conditions with their method due tochanges in light intensity; however, they fail to demonstrate a suitablesolution. Similarly, Webster et al. (Webster, J. B.; Duncan, S. E.;Marcy, J. E.; O'Keefe, S. F.; Effect of narrow wavelength bands of lighton the production of volatile and aroma-active compounds in ultra hightemperature treated milk. Int. Dairy Journal. 2011, 21, 305-311),studying the effects of light on milk, acknowledge the inability to makedirect comparisons between all experiments due to differences in lightenergy output as a limitation of their capability (see also Webster, J.B.; Duncan, S. E.; Marcy, J. E.; O'Keefe, S. F.; Controlling LightOxidation Flavor in Milk by Blocking Riboflavin Excitation Wavelengthsby Interference. J. Food Sci. 2009, 74, 3390-3398). As another example,in a study by Palanuk (Palanuk, S. L.; Warthesen, J. J.; Smith, D. E.;Effect of agitation, sampling location and protective films onlight-induced riboflavin loss in skim milk. J. Food Sci. 1988, 53,436-438), sampling location was shown to influence the results instudies of the effects of light on riboflavin in skim milk.

Additionally, studies in this field frequently require an extendedtesting period, such as days or weeks. For instance, Cladman (Cladman,W.; Scheffer, S.; Goodrich, N.; Griffiths, M. W.; Shelf-life of MilkPackaged in Plastic Containers With and Without Treatment to ReduceLight Transmission, Int. Dairy Journal. 1998, 8, 629-636) performed astudy on photoprotective properties of materials that required a twentyday period to expose the samples. As another example, while Saffert atal. report two studies (Saffert, A.; Pieper, G.; Jetten, J.; Effect ofPackage Light Transmittance on the Vitamin Content of Pasteurized WholeMilk. Packag. Technol. Sci. 2006, 19, 211-218; Saffert, A.; Pieper, G.;Jetten, J.; Effect of Package Light Transmittance on Vitamin Content ofMilk. Part 2: UHT Whole Milk. Packag. Technol. Sci. 2008, 21, 47-55)that explore package performance related to retaining nutrients in milk,they conducted the study under conditions that required days ofexposure.

There is an art recognized need to make determinations of the shelf lifeperformance of food products contained within packages (Sensory ShelfLife Estimation of Food Products, G. Hough, CRC Press: Florida, 2010).It is desirable to make these determinations in an accelerated format.One current approach to accelerated shelf life determinations involvesthe use of higher storage temperature to accelerate the reactions withinthe food that lead to shelf life decline; however, these methodsrequiring storage temperatures that are different from typical storageconditions are deficient in some cases as they do not allow the foodsystem to be under storage temperatures that are relevant for retailstorage. Furthermore, such higher storage temperatures may cause changesto the food (e.g., phase changes in the food system, changes to theconformations of molecules in this food) that limit the utility of suchmethods to provide accurate predictions in an accelerated time frame.

Given the above, a robust scientific method to rapidly quantifyphotoprotective performance of packaging concepts in a way that allowsrelative comparisons between the packaging concepts and is relevant tothe conditions used for such packaging concepts in their targeted rearworld applications is needed. These methods are needed to allow for thecreation of performance design models for packaging concepts, and toallow for efficient design of photoprotective packages that achieve therequired balance of performance attributes for a given package cost,weight, material usage, or other design requirements.

SUMMARY OF INVENTION

In one aspect, the present invention relates to a method for producingpackaging designs comprising: (a) providing a known homogeneous liquidsample comprising one or more known photosensitive entities atappropriate concentrations; (b) providing a cell having controlledoptical properties to contain the sample at a controlled temperaturebetween about −20° C. and about 100° C.; (c) providing a light sourcethat generates a light beam with a controlled spectral signature betweenabout 290 and about 1000 nm and a controlled intensity between about0.01 and about 5 W/cm²; (d) placing the sample into the cell, renderinga sample cell; (e) placing a first test material between the lightsource and the sample cell, rendering a shielded sample cell, whereinthe light beam impinges upon the first test material and any penetratinglight impinges upon the sample cell, and wherein the first test materialcomprises a known quantitative or qualitative property; (f) exposing theshielded sample cell to one or more light beam intensities for one ormore durations; (g) measuring the changes to the one or morephotosensitive entities contained within the shielded sample cell at oneor more durations to generate data points; (h) using the data points todetermine a photoprotective performance value of the first testmaterial; (i) repeating steps (a)-(h) with one or more additional testmaterials under the same conditions to generate one or more additionalphotoprotective performance values; (j) utilizing the two or morephotoprotective performance values, more preferably more than two, togenerate a model for a class of materials that relates that knownqualitative or quantitative property to the photoprotective performance;(k) using the model of step (j) to identify a photoprotectiveperformance value of a packaging material; and (l) using the packagingmaterial in step (k) to design a package.

In another respect the present invention relates to a method ofpredicting the photoprotective performance of a material, the methodcomprising: (a) providing a sample comprising one or more photosensitiveentities; (b) providing a cell having controlled optical properties tocontain the sample at a pre-determined temperature between about −20° C.and about 100° C.; (c) providing a light source that generates a lightbeam with a spectral signature between about 290 and about 1000 nm andan intensity between about 0.01 and about 5 W/cm² as measured at adefined monitoring position; (d) placing the photosensitive entity intothe cell, rendering a sample cell; (e) placing a first test materialbetween the light source and the sample cell, rendering a shieldedsample cell, wherein the light beam impinges upon the first testmaterial and any penetrating light impinges upon the sample cell, andwherein the first test material comprises a known quantitative orqualitative property; (f) exposing the shielded sample cell to one ormore light beam intensities for one or more durations; (g) measuring thechanges to the one or more photosensitive entities contained within theshielded sample cell at one or more durations to generate data pointseither by studying the sample while it is contained within said cell orby removing a sample for measurement by external methods; (h) using thedata points to determine a photoprotective performance value of thefirst test material; (i) repeating steps (a)-(h) with one or moreadditional test materials to generate one or more additionalphotoprotective performance values; (j) utilizing the two or morephotoprotective performance values to generate a model for a class ofmaterials with the known qualitative or quantitative property thatpredicts the photoprotective performance of an untested material withinthe same class based upon the known quantitative or qualitative propertyof the untested material; (k) using the model of step (j), for materialsof the same class, predicting the unknown qualitative or quantitativeproperty of a material based upon a known photoprotective performancevalue or predicting an unknown photoprotective performance value basedupon a known qualitative or quantitative property of the material; (l)optionally using one or more values from step (h) or one or more modelsof step (j) to create a library of data which includes thephotoprotective performance values as well one or more values describingthe qualitative or quantitative properties of the material; (m)optionally using the library from step (l) to identify, describe, and/orpredict properties of materials.

In still another respect, the present invention relates to a method forpredicting the photoprotective performance of a material, the methodcomprising: (a) providing a known homogeneous liquid sample comprisingone or more known photosensitive entities at appropriate concentrations;(b) providing a cell having controlled optical properties to contain thesample at a controlled temperature between about −20° C. and about 100°C.; (c) providing a light source that generates a light beam with acontrolled spectral signature between about 290 and about 1000 nm and acontrolled intensity between about 0.01 and about 5 W/cm²; (d) placingthe sample into the cell, rendering a sample cell; (e) placing a firsttest material between the light source and the sample cell, rendering ashielded sample cell, wherein the light beam impinges upon the firsttest material and any penetrating light impinges upon the sample cell,and wherein the first test material comprises a known quantitative orqualitative property; (f) exposing the shielded sample cell to one ormore light beam intensities for one or more durations; (g) measuring thechanges to the one or more photosensitive entities contained within theshielded sample cell at one or more durations to generate data points;(h) using the data points to determine a photoprotective performancevalue of the first test material; (i) repeating steps (a)-(h) with oneor more additional test materials under the same conditions to generateone or more additional photoprotective performance values; (j) utilizingthe two or more photoprotective performance values, more preferably morethan two, to generate a model for a class of materials that relates thatknown qualitative or quantitative property to the photoprotectiveperformance; and (k) using the model of step (j), to identify aphotoprotective performance value of a packaging material.

In certain embodiments, the quantitative or qualitative propertycomprises one or more of the following: whiteness index ASTM E313;brightness index ASTM D985; CIE (1976) L*a*b* tri-stimulus data ASTMdesignation of E313-10, D2244, E1347, E1349, E1477, E2214, E284, E308,E805, E991, E1331, E275, D2616, D2745, D3134, D3964, D4877, D6290;DuPont Appearance Analyzer data; Diffuse Brightness of Paper andPaperboard (d/0) ASTM D2470; Standard test for haze for plastics ASTMD1003; Brightness, Directional (TAPPI) (T452); Brightness Directional;Brightness, Diffuse (T525); Brightness with Color (Diffuse (Micro TB1C)or Directional/TAPPI (MicroS-5)); Printing & calculated TAPPI Opacity,Scattering & Absorption Coefficients, Sheet Brightness (T519);Directional/TAPPI Opacity, Scattering Coefficient, AbsorptionCoefficient (T425); T/dyne Micro TB-1C: Diffuse brightness, opacity,color, color difference, ASTM Index, & tristimulus; T/dyne Micro S-5BOC:Dir/TAPPI brightness, opacity, color, color difference, ASTM Index,& tristimulus; Color, Hunter or CIE L*A*B* (State Directional or DiffuseValues), packaging material composition (e.g., polymer matrix forplastic packaging), packaging design parameters (e.g., film thicknessfor sheet material), opacifying agent composition(s), and/or opacifyingagent(s) loading as a design parameter.

In certain embodiments, preferred package designs providing efficientdelivery of the desired properties (e.g., low cost materials, reducedweight materials) can be identified using the method.

In additional embodiments, the predictive models are used to facilitatethe design of packages that provide photoprotection specific for thepackage contents. In other embodiments, the predictive models are usedto assess sustainability metrics for a package design. For example, themethods could be used to allow for quantification, and thusoptimization, of the impacts of parameters in package design (e.g.,packaging material reduction or selection of alternative materialchoices) on sustainability impacts. In certain particular examples, thepackage contents include food, beverages, drugs, pharmaceuticals, and/orother nutrient-containing products.

In certain embodiments, the method further comprises correlating thechanges to the one or more photosensitive entities in step (g) with oneor more sensory evaluation criteria values and utilizing said predictedphotoprotective performance to further predict one or more sensoryevaluation criteria values of an untested material. Sensory evaluationscomprise human evaluation of one or more of the following criteria:taste, texture, odor, or appearance.

In additional embodiments, the library of data of step (l) and (m) canbe used to aid in predicting packaging designs that provide desiredqualitative or quantitative properties.

In further embodiments, the sample is maintained under one or both ofcontrolled atmosphere conditions and under agitation, the exteriorsurface of the sample cell is maintained free from condensate, and/orthe light beam is collimated.

In some embodiments, the one or more photosensitive entities areconstituents of food, beverages, drugs, pharmaceuticals, or othernutrient-containing products. In other embodiments, the sample comprisesone or more photosensitive entities selected from natural and syntheticfood additives, dyes, and pigments; chlorophyll; myoglobin,oxymyoglobin, and other hemeproteins; water and fat soluble essentialnutrients, minerals, and vitamins; food components containing fattyacids; oils; proteins; pharmaceutical compounds; personal care andcosmetic formulation compounds and components; household chemicals andtheir components; and agricultural chemicals and their components. Inadditional embodiments, the sample comprises one or more photosensitiveentities selected from 2, 3, 4, 5, 6, 7, 8, 9, 10, or more of the givenclasses.

In further embodiments, the measuring comprises a test method selectedfrom the group consisting of HPLC (high performance liquidchromatography), GC (gas chromatography), IR (infrared) spectroscopy,NMR (nuclear magnetic resonance) spectroscopy, UV-VIS (ultra-violet,visible) spectroscopy, colorimetry, MS (mass spectrometry) coupled withother techniques (e.g., GC-MS and LC-MS), fluorescence spectroscopy, ionchromatography, thin layer chromatography (TLC), analytical wetchemistry, and/or electrochemical analysis (e.g., polarography,voltammetry). In particular embodiments, the measurement method is HPLCbased which involves removal of a test aliquot from the sample cell. Inanother embodiment, the measurement method is UV-VIS spectroscopy basedwhen sample analysis is performed while it is contained within thesample cell.

In another aspect, the present invention relates to a device forquantifying the photoprotective performance of a material, the devicecomprising: (a) a sample supply and control apparatus comprising: (I) acell having controlled optical properties, the cell capable ofcontaining a sample comprising one or more photosensitive entities; (II)a sample temperature sensor for monitoring the temperature of the samplewithin the cell; (III) a temperature control for maintaining the cell ata specified temperature set point that is chosen from the range betweenabout −20° C. and about 100° C. with a deviation about the set point ofless than 1° C.; (IV) a dry air supply for delivering low-humidity airto one or more exposed surfaces of the cell; and (V) an agitator formaintaining sample homogeneity within the cell; (b) a light generationand control apparatus comprising: (I) a light source, wherein the lightsource generates a light beam with a spectral signature between about290 and about 1000 nm and an integrated intensity between about 0.01 andabout 5 W/cm² as measured at a defined monitoring position; (II) a lightbeam collimating lens; (III) an infrared filter; (IV) a shutter; and (V)an iris; and (c) a test material holder positioned between the lightgeneration and control apparatus and the sample supply and controlapparatus such that, when a test material is placed in the test materialholder, the light beam impinges upon the test material and anypenetrating light impinges upon the cell.

In certain embodiments, the agitator comprises a magnetic stir barwithin the cell and a magnetic stirring motor positioned below the cell.In other embodiments, the sample supply and control apparatus furthercomprises an atmosphere control and monitoring apparatus within thecell, wherein the atmosphere control and monitoring apparatus comprisesa gas supply and metering device and an atmospheric sensor. In stillfurther embodiments, the light generation and control apparatus furthercomprises a spectral filter.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 illustrates an embodiment of an apparatus useful in the presentinvention.

FIGS. 2A-2C illustrate more detailed views of particular elements of theillustrative embodiment of FIG. 1.

FIG. 3 illustrates an embodiment of a sample cell useful in the presentinvention.

FIG. 4 illustrates an embodiment of a dry-air supply apparatus useful inthe present invention.

FIG. 5 illustrates a plot of the natural log of riboflavin concentrationversus light exposure time for one exemplary test material experiment.

FIG. 6 illustrates a plot of mean pseudo-first order rate constantversus mean TAPPI opacity for one exemplary test material experiment.

DETAILED DESCRIPTION

It is to be understood that this invention is not limited to particularembodiments, which can, of course, vary. It is also to be understoodthat the terminology used herein is for the purpose of describingparticular embodiments only, and is not intended to be limiting.Further, all publications referred to herein are incorporated byreference herein for the purpose cited to the same extent as if each wasspecifically and individually indicated to be incorporated by referenceherein.

As used in this specification and the appended claims, terms in thesingular and the singular forms “a,” “an,” and “the,” for example,include plural referents unless the content clearly dictates otherwise.Thus, for example, reference to “photosensitive entity,” “thephotosensitive entity,” or “a photosensitive entity” also includes aplurality of photosensitive entities. Use of the term “a photosensitiveentity” also includes, as a practical matter, many molecules of thatphotosensitive entity.

Additionally, as used herein, “comprising” is to be interpreted asspecifying the presence of the stated features, integers, steps, orcomponents as referred to, but does not preclude the presence oraddition of one or more features, integers, steps, or components, orgroups thereof. Thus, for example, a sample comprising a photosensitiveentity may contain additional photosensitive entities or othercomponents, such as other non-photosensitive nutrients. Additionally,the term “comprising” is intended to include examples encompassed by theterms “consisting essentially of” and “consisting of.” Similarly, theterm “consisting essentially of” is intended to include examplesencompassed by the term “consisting of.”

The present invention relates to devices and methods useful fordetermining and/or quantifying photoprotective properties of a material,and for making meaningful comparisons between such properties, byquantifying the light-induced change or degradation of one or morephotosensitive entities. In another embodiment, the method involvescorrelating a photoprotective performance value with another knownquantitative or qualitative property of a material, such as thematerial's TAPPI Opacity or titanium dioxide content, to produce apredictive or correlative model. In another embodiment, the methodfurther involves using the predictive or correlative model to assign apredicted photoprotective performance value to an untested materialwithin the same material class based on the correlative property value.

The present apparatus allows for the accelerated testing ofphotoprotective properties. In certain embodiments, a test can beperformed in a matter of hours while simulating the light exposure ofseveral weeks under commercial food storage conditions. This method canthus be considered a high throughput screening method, as the testingrates can be accelerated over 100 times. In certain embodiments, themethod and apparatus can be used to determine an optimum amount oflight-protective agent, such as TiO₂, to include in a packaging materialfor photo-protection.

In certain other embodiments, this method can be useful to predict theresults of sensory evaluations for a certain package concept, therebyavoiding the time and resources required to actually conduct sensoryevaluation research on that package concept. Typically sensoryevaluation research with human subject appraisers requires a largenumber of panelists and product assessments due to the limitations ofthe panelists' ability to accurately and precisely detect differences insensory qualities of a product. As such, this type of evaluation of apackaging concept is generally both time consuming and costly. By usingthe methods of the present invention to predict the results of such asensory evaluation, the present invention allows for predicted sensoryevaluation results to be obtained in an accelerated timeframe and at adecreased cost.

The method and apparatus also provide a means to compare not-in-kindprotective packaging solutions. For example, polymeric packaging filmscan be compared to paperboard.

FIGS. 1 and 2A-2C illustrate one possible embodiment of an apparatus ofthe present invention which is useful in the disclosed methods. Theindividual components of the overall apparatus are contained within anenclosure 60, which is generally light blocking with regard to thespectra being analyzed during an experiment. To maintain properatmospheric conditions (temperature, humidity, etc.) within theenclosure, the enclosure 60 possesses an exhaust fan and fan trunk 58,which allows the air within enclosure 60 to be cycled at a desiredinterval and/or rate.

Within enclosure 60, a light source, such as a lamp (not shown)contained within lamp housing 16, is connected via appropriateelectrical connections (not shown) to a light source power supply 14,which in turn is connected via appropriate electrical connections (notshown) to a lamp controller 10.

The light source can be any suitable light source to produce the desiredlight intensity, stability, and spectral characteristics. Depending uponthe needs of the experiment, light sources employed may includeincandescent light sources, fluorescent light sources, arc dischargelamps, LEDs (light emitting diodes), and/or laser light sources. Forexample, these light sources include but are not limited to carbon arc,mercury vapor, xenon arc, tungsten filament, or halogen bulbs. In oneparticular embodiment, the light source is a xenon arc lamp.

In certain embodiments, the light source is capable of providing anintensity of between about 0.001 W/cm² and about 5 W/cm² as measured atthe defined monitoring position. In other embodiments, the light sourceis capable of providing an intensity of at least about 0.001 W/cm²,0.005 W/cm², 0.007 W/cm², 0.01 W/cm², 0.05 W/cm², 0.1 W/cm², 1 W/cm²,2.5 W/cm², or 5 W/cm² as measured at the defined monitoring position. Infurther embodiments, the light source is capable of providing anintensity of not more than about 0.001 W/cm², 0.005 W/cm², 0.007 W/cm²,0.01 W/cm², 0.05 W/cm², 0.1 W/cm², 1 W/cm², 2.5 W/cm², or 5 W/cm² asmeasured at the defined monitoring position. In further embodiments, thelight source is capable of providing an intensity between about 0.005W/cm² and about 4 W/cm², between about 0.007 W/cm² and about 3 W/cm²,between about 0.01 W/cm² and about 2.5 W/cm², between about 0.05 W/cm²and about 2 W/cm², or between about 0.1 W/cm² and about 1 W/cm² asmeasured at the defined monitoring position.

In other embodiments, the light source is capable of producing lightwith a spectral signature of about 200 nm to about 2000 nm. In otherembodiments, the light source is capable of providing light at awavelength of at least about 200 nm, 220 nm, 240 nm, 260 nm, 280 nm, 290nm, 300 nm, 350 nm, 400 nm, 450 nm, 500 nm, 550 nm, 600 nm, 650 nm, 700nm, 750 nm, 800, nm, 900 nm, 1000 nm, 1250 nm, 1500 nm, 1750 nm, or 2000nm. In further embodiments, the light source is capable of providinglight at a wavelength of not more than about 200 nm, 220 nm, 240 nm, 260nm, 280 nm, 290 nm, 300 nm, 350 nm, 400 nm, 450 nm, 500 nm, 550 nm, 600nm, 650 nm, 700 nm, 750 nm, 800, nm, 900 nm, 1000 nm, 1250 nm, 1500 nm,1750 nm, or 2000 nm. In still further embodiments, the light source iscapable of providing a spectral signature of about 220 nm to about 1750nm, about 240 to about 1500 nm, about 260 to about 1250 nm, about 290 toabout 1000 nm, about 200 to about 400 nm, about 350 to about 750 nm, orabove about 750 nm.

In certain embodiments, the intensity and/or spectral characteristics ofthe light source are controlled and/or modified by one or more of alens, a water-based infrared filter (to reduce the heat signature of thelight beam), and a spectral filter. In one particular embodiment, thelight from a lamp within lamp housing 16 travels through a collimatinglens assembly 20, then through an infrared filter 22, which is awater-based infrared filter attached to water reservoir 34 and waterpump 36, the flow volume of which is controlled by a pump flowcontroller 4 to which water pump 36 is attached via appropriateelectrical connections. The collimated and infrared-filtered light thentravels through an optical filter holder 24, which can optionallycontain an optical filter or filters to attenuate the light beam orportions thereof. Though the lens, infrared filter, and spectralfilter(s) are shown in a particular order in FIGS. 1 and 2 a, this isnot to be taken as an indication that all of these components arerequired, nor that the indicated order is required. These componentscould be used in any desired order and/or in any desired combination,including employing none of them in the apparatus and method of theinvention.

In certain embodiments, within enclosure 60, a light source, such as alamp (not shown) contained within lamp housing 16, is connected viaappropriate electrical connections (not shown) to a light source powersupply 14, which in turn is connected via appropriate electricalconnections (not shown) to a lamp controller 10. Lamp feedback monitor18 is electrically connected to the lamp controller 10. The lampfeedback monitor 18 communicates with the lamp controller 10 which inturn communicates with the light source power supply 14 to adjust theamount of power provided to the light source and/or in order to adjustthe intensity of the light emanating from the light source.

In one embodiment, to ensure that the light beam possesses the properintensity, a light power density sensor 30 can be positioned within thelight beam, for instance removably positioned, using one of a pluralityof holders 31 located along light path 33. In a preferred embodiment,the light power sensor 30 can be removably positioned within the lightbeam using holder 72 and a suitably designed support apparatus. Thelight power density sensor 30 is attached via appropriate connections(not shown) to the optical energy meter 12. Light power density sensor30 can be inserted into an appropriate holder, so that a discreteintensity reading can be taken, for instance, prior to the initiation ofan experiment and again after the termination of an experiment and/or attimes during an experiment. This would allow the intensity of the lightbeam to be tested both before and after an experiment so that the usercan ensure that the power intensity was correctly set and did notsignificantly increase or decrease throughout the experiment.

In other embodiments, in order to ensure that the light beam possessesthe proper spectral characteristics, a spectrometer sensor 32 can beremovably positioned within the light beam using one of a plurality ofholders 31 located along light path 33 or by using holder 72 and asuitably designed support apparatus. The spectrometer sensor 32 isattached via appropriate connections (not shown) to a spectrometer 8.Spectrometer sensor 32 can be inserted into an appropriate holder, sothat a discrete spectrometry reading can be taken, for instance, priorto the initiation of an experiment and again after the termination of anexperiment. This would allow the spectral characteristics of the lightbeam to be tested before and after an experiment so that the user canensure that the spectral characteristics were as desired and stable inthe time frame of the experiment.

In another embodiment, part of the light beam can be directed away fromlight path 33 towards a suitable monitoring position (not shown) so asto allow monitoring of the light beam intensity and/or spectralcharacteristics during an experiment.

Light exposure initiation and cessation during operation of theapparatus or method can be controlled, for example, by a shuttermechanism 26, the operation of which is controlled by a shuttercontroller 6, to which it is attached via appropriate connections (notshown). Further, the cross sectional area of the light beam impingingupon a test material and/or sample can be adjusted by an iris 28 locatedwithin one of the plurality of holders 31, which can be opened andclosed as needed to produce a light beam of the desired diameter. Again,though these components are illustrated in FIG. 1, this should not betaken as an indication that one or all of them is required. Forinstance, the apparatus could be operated without a shutter by simplycontrolling initiation of the light beam through the lamp controller 10and/or light source power supply 14. Similarly, the size of the lightbeam could be alternatively controlled, for example, through thecollimating lens 20.

Looking at FIGS. 1, 2 b, and 2 c, after passing through the iris 28, thelight beam will impinge upon a test material 38 a, which is held inplace by material holder 38 b, which is in turn located within one ofthe plurality of holders 31 or more preferably holder 72. The testmaterial 38 a can be a material with certain known qualitative orquantitative properties, such as TAPPI Opacity or titanium dioxidecontent, or can be a completely unknown material. Further, the testmaterial 38 a can be any material suitable for use as a packagingmaterial or photoprotective material. Such materials include plastics(polymeric materials, e.g., low density polyethylene), glass, metal(e.g., cans, foils, or metalized layers), cellulosic materials (e.g.,paper, paperboard), or combinations thereof in forms such as laminatedstructures, films (e.g., plastic wraps), sheets (e.g., papers), bags,sleeves, pouches, or rigid structures (e.g., bottles). These materialsmay also contain additives (e.g., pigments, printing inks, antioxidants)to provide additional appearance attributes or functionalities to thematerial. In certain embodiments, the material contains titaniumdioxide. Materials tested could comprise an actual package, a portion ofa packaging material, or a prototype of a portion of a packaging systemincluding films, foils, plaques from rigid parts, papers, and laminatedor composite structures of these materials.

The light transmitted by the test material 38 a will in turn impingeupon the sample cell 44, which will be held in place during theexperimental run by sample cell holder 42, which optionally can beinsulated so that it retains temperature more efficiently andeffectively. Sample cell holder 42 is in direct contact with heattransfer block 48, which is attached to thermoelectric device 50, underthe control of thermoelectric controller 51. Thermoelectric device 50can be either a heater or cooler, or a device that is capable of bothheating and cooling. During operation, thermoelectric controller 51directs a temperature set point for thermoelectric device 50. Throughheat transfer block 48, the temperature gradient (cold or heat)generated by thermoelectric device 50 is transferred to sample cellholder 42. This allows the temperature within sample cell 44 to bemaintained at a fixed temperature throughout an experimental run.Optionally, a heat transfer compound can be used to facilitate heattransfer between the sample cell 44 and the sample cell holder 42. Incertain embodiments, the temperature can be set at a temperature betweenabout −20° C. and about 100° C. In other embodiments, the temperaturecan be set at a temperature of at least about −20° C., −10° C., −5° C.,−2° C., 0° C., 1° C., 2° C., 3° C., 4° C., 5° C., 6° C., 7° C., 8° C.,10° C., 25° C., 50° C., or 100° C. In further embodiments, thetemperature can be set at a temperature of not more than about −20° C.,−10° C., −5° C., −2° C., 0° C., 1° C., 2° C., 3° C., 4° C., 5° C., 6°C., 7° C., 8° C., 10° C., 25° C., 50° C., or 100° C. In still furtherembodiments, the temperature can be set at between about −10° C. andabout 50° C., about −5° C. and about 25° C., about −2° C. and about 10°C., about 0° C. and about 8° C., about 1° C. and about 7° C., about 2°C. and about 6° C., about 3° C. and about 5° C. In certain otherembodiments, the temperature is set at about 4° C. In an embodiment, thedeviation about the temperature set point is less than 1° C.

Sample cell 44 can comprise any suitable material and shape such that itpossesses the desired optical characteristics. Preferably, sample cell44 is optically transparent in the spectral range being investigatedduring the experiment. In certain embodiments, sample cell 44 is made ofquartz. In certain embodiments, such as those shown in FIGS. 1 and 3,sample cell 44 can be substantially flat on one end, thereby allowingthe light to impinge upon the sample cell at an angle that issubstantially perpendicular to the flat end of the sample cell 44, whichcan be a desirable optical situation. In certain embodiments, such asthose shown in FIGS. 1 and 3, sample cell 44 can also be equipped withone or more access ports 43 to allow test samples, additives, or gasesto be added or withdrawn from the cell and/or to allow sample cellthermocouple 56 or other probes or sensors to be inserted into samplecell 44 during an experimental run. Sample cell thermocouple 56 is inturn attached, via appropriate connections, to temperature meter 62.This can allow the temperature of the sample to be monitored and/orcontrolled throughout an experimental run. In certain embodiments,thermocouple 56 and/or temperature meter 62 is placed in communicationwith thermoelectric controller 51 such that the temperature can beautomatically adjusted throughout an experimental run to maintain thesample at the desired temperature.

Further, access ports 43 could allow for an optional gas delivery tubeand/or atmospheric sensor (not shown) to be inserted into the samplecell during an experimental run for monitoring and/or controlling theatmospheric conditions within sample cell 44 throughout the experimentalrun. Additionally, as illustrated in FIGS. 1 and 2 b, directly belowinsulated sample cell holder 42 is a magnetic stirring motor 40, whichis attached via appropriate connections to a magnetic stirrer speedcontroller 54. As shown in FIG. 3, this allows a magnetic stir bar 45 tobe located within sample cell 44 during an experimental run so that themagnetic stirring motor can effectuate agitation of the sample at adesired speed throughout an experimental run, thereby ensuringsubstantial sample homogeneity.

As shown in FIG. 1, in certain embodiments, dry air, meaning air withrelatively low humidity, can be supplied to the front and/or rear facesof sample cell 44 via delivery tubes 46 in order to prevent or reducecondensation forming on the sample cell. As used herein, the term meansatmospheric air or any other suitable gas, such as gaseous nitrogen.With reference to FIG. 4, air is supplied from supply 64, via feed line65, to pressure regulator 66. The air then continues through feed line65 to flow valve 68 before traveling through drying chamber 70. Dryingchamber 70 can be any suitable type of apparatus for reducing humidityin air. For instance, in certain embodiments, drying chamber 70 can be apolycarbonate tube filled with a desiccant, such as “Drierite”desiccant. The reduced-humidity air then exits the drying chamber andtravels via delivery tubes 46 to the faces of sample cell 44, where itis released.

Any light that passes completely through sample cell 44 will eventuallyimpinge upon beam stop 52, which is constructed in such a way that itcaptures substantially all remaining light without allowing anysignificant portion of the light to reflect back toward the sample cell.

In certain embodiments, one or more of the components of the overallapparatus may be controlled or monitored by computer 2. This can includeone or more of light source power supply 14, lamp controller 10, pumpflow controller 4, water pump 36, lamp output feedback detector 18,optical energy meter 12, shutter mechanism 26, shutter controller 6,iris 28, spectrometer 8, thermocouple 56, temperature meter 62,thermoelectric controller 51, magnetic stirrer speed controller 54, gassupply and metering device and atmospheric sensor (not shown), airsupply 64, or pressure regulator 66.

During operation of the apparatus disclosed herein, a test material 38 ais placed within the material holder 38 b and one or more photosensitiveentities is placed in the sample cell. Examples of test materials thatcan be investigated using the present invention include plastics(polymeric materials, e.g., low density polyethylene), glass, metal(e.g., cans, foils, or metalized layers), cellulosic materials (e.g.,paper, paperboard), or combinations thereof in forms such as films(e.g., plastic wraps), sheets (e.g., papers), laminate structures, bags,sleeves, pouches, or rigid structures (e.g., bottles). These materialsmay also contain additives (e.g., pigments, printing inks, antioxidants)to provide additional appearance attributes or functionalities to thematerial. Packaging concepts would be comprised of these same materialsand could comprise an actual package, a portion of a packaging material,or a prototype of a portion of a packaging system including films,foils, plaques from rigid parts, papers, and laminated or compositestructures of these materials.

In certain embodiments, the photosensitive entity is a photosensitivenutrient. In particular embodiments, the photosensitive entity isselected from:

-   -   i. natural and synthetic food additives, dyes, and pigments        (e.g., curcumin, erythrosine);    -   ii. chlorophyll (all variants);    -   iii. myoglobin, oxymyoglobin, and other hemeproteins;    -   iv. water and fat soluble essential nutrients, minerals, and        vitamins (e.g., riboflavin, vitamin A, vitamin D);    -   v. food components containing fatty acids, particularly        polyunsaturated fatty acids;    -   vi. oils (e.g., soybean oil);    -   vii. proteins (e.g., proteins derived from the amino acids        tryptophan, histidine, tyrosine, methionine, cysteine, etc.);    -   viii. pharmaceutical compounds;    -   ix. personal care and cosmetic formulation compounds and their        components;    -   x. household chemicals and their components; and    -   xi. agricultural chemicals and their components.

The species of interest could be studied in neat form or as a componentof a solution or formulation. In certain embodiments, multiplephotosensitive entities could be present, each at differentconcentrations. Different modes of light-induced change or degradationcould occur in the system based upon the chemical nature of thephotosensitive entities present to participate in the changes. Forcomplete food systems, a combination of fats, oxygen, and photosensitivenutrients could be present to allow the interplay between multiplephotosensitive entities and associated species to be observed upon lightexposure. To allow for targeted and focused study as well as ease ofanalysis, model systems could be employed where only a single or a fewcomponents are included such that effects are isolated to a lessernumber of components. Within a model system, a combination of entitiesthat interact with light via different mechanisms could allow for amultidimensional assessment of light protection performance via a singleexperiment without the complexities of the study of a complete foodsystem. Entities that interact with light via pathways that involvesinglet oxygen represent one class of photosensitive entities. Such alist is disclosed by Min et al. (Min, D. B. and Boff, J. M.; Chemistryand Reaction of Singlet Oxygen in Foods. CRFSFS, 2002, 1, 58-72). Otherentities that themselves are photosensitizers (e.g., riboflavin), mayallow for a different learning regarding light protection performance.In another embodiment, combinations of entities could be used to affectthe rate of the changes occurring to the entities, for example theinclusion of a photosensitizer may accelerate the effect while theinclusion of an antioxidant may retard the effect. Thus, in certainembodiments, a single photosensitive entity could be present in thesample cell, while, in other embodiments, at least 2, 3, 4, 5, 6, 7, 8,9, 10, 11, 12, 13, 14, 15, or more photosensitive entities can besimultaneously present and/or studied. In particular embodiments, thephotosensitive entities present and/or studied include one or morephotosensitive entities from 2, 3, 4, 5, 6, 7, 8, 9, 10 or all 11 ofeach of the following classes:

-   -   i. natural and synthetic food additives, dyes, and pigments        (e.g., curcumin, erythrosine);    -   ii. chlorophyll (all variants);    -   iii. myoglobin, oxymyoglobin, and other hemoproteins;    -   iv. water and fat soluble essential nutrients, minerals, and        vitamins (e.g., riboflavin, vitamin A, vitamin D);    -   v. food components containing fatty acids, particularly        polyunsaturated fatty acids;    -   vi. oils (e.g., soybean oil);    -   vii. proteins (e.g., proteins derived from the amino acids        tryptophan, histidine, tyrosine, methionine, cysteine, etc.);    -   viii. pharmaceutical compounds;    -   ix. personal care and cosmetic formulation compounds and their        components;    -   x. household chemicals and their components; and    -   xi. agricultural chemicals and their components.        In particular embodiments, the photosensitive entities studied        include one or more photosensitive entities from class i and ii,        class i and iii, class i and iv, class i and v, class i and vi,        class and vii, class i and viii, class i and ix, class i and x,        class i and xi, class ii and iii, class ii and iv, class ii and        v, class ii and vi, class ii and vii, class ii and viii, class        ii and ix, class ii and x, class ii and xi, class iii and iv,        class iii and v, class iii and vi, class iii and vii, class iii        and viii, class iii and ix, class iii and x, class iii and xi,        class iv and v, class iv and vi, class iv and vii, class iv and        viii, class iv and ix, class iv and x, class iv and xi, class v        and vi, class v and vii, class v and viii, class v and ix, class        v and x, class v and xi, class vi and vii, class vi and viii,        class vi and ix, class vi and x, class vi and xi, class vii and        viii, class vii and ix, class vii and x, class vii and xi, class        viii and ix, class viii and x, class viii and xi, class ix and        x, class ix and xi, class x and xi, or any combination thereof.

Each photosensitive entity can be present at a concentration of, forexample, 0.0000001 wt % to 100 wt %. In certain embodiments, thephotosensitive entity is present at a concentration of at least about0.0000001 wt %, 0.000001 wt %, 0.00001 wt %, 0.0001 wt %, 0.001 wt %,0.01 wt %, 0.01 wt %, 0.1 wt %, 1.0 wt %, 2.0 wt %, 3.0 wt %, 4.0 wt %,5.0 wt %, 10.0 wt %, 20.0 wt %, 30.0 wt %, 40.0 wt %, 50.0 wt %, 60.0 wt%, 70.0 wt %, 80.0 wt %, 90.0 wt %, 95.0 wt %, 99.0 wt %, or 100.0 wt %.In certain embodiments, the photosensitive entity is present at aconcentration of less than about 100.0 wt %, 99.0 wt %, 95.0 wt %, 90.0wt %, 80.0 wt %, 70.0 wt %, 60.0 wt %, 50.0 wt %, 40.0 wt %, 30.0 wt %,20.0 wt %, 10.0 wt %, 5.0 wt %, 4.0 wt %, 3.0 wt %, 2.0 wt %, 1.0 wt %,0.1 wt %, 0.01 wt %, 0.001 wt %, 0.0001 wt %, 0.00001 wt %, 0.000001 wt%, or 0.0000001 wt %. The concentrations are dependent upon the speciesunder evaluation and its typical concentration during practicalapplications and uses.

The sample cell and sample contained therein are brought to anappropriate temperature for the test, for example a temperature betweenabout −20° C. and about 100° C. Light produced by the light source,which has been optionally collimated, filtered, focused, and/or sized,at a desired intensity (e.g., 0.01-5 W/cm² as measured at the definedmonitoring position) and wavelength (e.g., 290-1000 nm) is then made toimpinge upon the test material. The light that passes through the testmaterial in turn impinges upon sample cell 44 and the sample containedtherein.

Because the one or more entities within sample cell 44 arephotosensitive, the light impinging upon them will cause some level ofchange which can be quantified at desired intervals either by measuringthe sample while it is contained within sample cell 44 or by removing atest aliquot for measurement by external methods. Suitable analyticalmethods for determining the amount of light-induced change ordegradation include HPLC (high performance liquid chromatography), GC(gas chromatography), IR (infrared) spectroscopy, NMR (nuclear magneticresonance) spectroscopy, UV-VIS (ultra-violet, visible) spectroscopy,colorimetry, MS (mass spectrometry) coupled with other techniques (e.g.,GC-MS and LC-MS), fluorescence spectroscopy, on chromatography, thinlayer chromatography (TLC), analytical wet chemistry, and/orelectrochemical analysis (e.g., polarography, voltammetry). Inparticular embodiments, the measurement method is HPLC based whichinvolves removal of a test aliquot from sample cell 44. In anotherembodiment, the measurement method is UV-VIS spectroscopy based whensample analysis is performed while it is contained within sample cell44. The experiment is continued for the desired length of time, withmeasurements performed at the desired intervals. The run time is afunction of the nature of the photosensitive entity, environmentalconditions (e.g., temperature and gas modification), and the analyticalstudy of its associated rate of change. In certain embodiments, theexperimental run time is less than 12 hours, less than 11 hours, lessthan 10 hours, less than 9 hours, less than 8 hours, less than 7 hours,less than 6 hours, less than 5 hours, less than 4 hours, less than 3hours, less than 2 hours, less than 1 hour, less than 45 minutes, orless than 30 minutes.

The sampling intervals should be selected to obtain a minimum of twodata points. In particular embodiments, the sampling intervals areselected to obtain at least 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15,16, 17, 18, 19, 20, 25, 30, 35, 40, 45, or 50 data points. In certainembodiments the data points are distributed based on the anticipatedsample reaction kinetics. Selected intervals will thus be dependent uponthe rate of change of the photosensitive entities. In certainembodiments, the samples are extracted automatically via syringe pump orother suitable device and are delivered directly to vials or analyticalequipment for analysis.

Once two or more measurements have been performed for an experimentalrun, the resulting data points tracking the change of the photosensitivemolecule(s) or derivative product(s) can be used to assign aphotoprotective performance value to the test material. Suchphotoprotective values can include, for instance, a pseudo-first orderrate constant for light-induced change or degradation of thephotosensitive entity being examined which can be converted to a lightprotection factor (LPF) via a suitable mathematical transformation. Forexample, LPF could be defined as the half-life of a photosensitiveentity which is calculated, for example, for pseudo first order reactionkinetics by dividing ln(2) by the obtained pseudo-first order rateconstant. Moreover, by regulating the variables of the experimentalruns, such as light spectra, light intensity, light focus, duration oflight exposure, sample temperature, sample homogeneity, and sampleatmospheric conditions, results can be obtained with sufficient accuracyand precision to allow for quality run-to-run comparisons to be made.

In certain other embodiments, numerous test materials with one or moreknown quantitative or qualitative properties can be evaluated and aphotoprotective performance value assigned to each such test material.The known quantitative or qualitative property can be, for example, aknown concentration of opacifying agent (e.g., TiO₂) or a knownreflectivity or opacity value (e.g., a TAPPI opacity value). In certainexamples, the known quantitative or qualitative property or propertiesutilized in the predictive model include one or more of the following:whiteness index ASTM E313; brightness index ASTM D985; CIE L*a*b*tri-stimulus data ASTM designation of E313-10, D2244, E1347, E1349,E1477, E2214, E284, E308, E805, E991, E1331, E275, D2616, D2745, D3134,D3964, D4877, D6290; DuPont Appearance Analyzer data; Diffuse Brightnessof Paper and Paperboard (d/0) ASTM D2470; Standard test for haze forplastics ASTM D1003; Brightness, Directional (TAPPI) (T452); BrightnessDirectional; Brightness, Diffuse (T525); Brightness with Color (Diffuse(Micro TB1C) or Directional/TAPPI (MicroS-5)); Printing & calculatedTAPPI Opacity, Scattering & Absorption Coefficients, Sheet Brightness(T519); Directional/TAPPI Opacity, Scattering Coefficient, AbsorptionCoefficient (T425); T/dyne Micro TB-1C: Diffuse brightness, opacity,color, color difference, ASTM Index, & tristimulus; T/dyne Micro S-5BOC:Dir/TAPPI brightness, opacity, color, color difference, ASTM Index,& tristimulus; Color, Hunter or CIE L*A*B* (State Directional or DiffuseValues), opacifying agent composition(s), and/or opacifying agent(s)loading. Where a correlative behavior is observed and modeled for aclass of materials, the model can be used for a material within saidclass to predict the unknown qualitative or quantitative property ofsaid materials based upon a known photoprotective performance value orto predict their an unknown photoprotective performance value based upona known qualitative or quantitative property of the material. Forinstance, several test materials with known TAPPI opacity values and/orknown TiO₂ concentrations could be evaluated using the apparatus andmethods disclosed herein, and a photoprotective performance valueassigned to each. The photoprotective performance values and TAPPIopacity or TiO₂ concentration values of these materials can in turn beused to generate a metric or predictive model that predicts thephotoprotective performance value of a material based upon its TAPPIopacity and or TiO₂ concentration. This metric or model can then be usedto predict the photoprotective performance vale of an untested materialof the same class with a known TAPPI Opacity or TiO₂ content.

EXAMPLES Example 1

The ANOVA Gage Repeatability and Reproducibility (R&R) measurementsystems analysis methodology employs an analysis of variance (ANOVA)random effects model to understand the measurement system capability. Ina Gage R&R study, the amount of variability observed in measurements bythe measurement system is compared to the total variability observed.Gage R&R study methodologies were applied to assess the repeatabilityand reproducibility of the device of FIG. 1 used with the methoddisclosed herein. This study was designed and analyzed using Minitabsoftware.

The Gage R&R study involved replicate evaluation of each of five filmsamples, or parts, by two different operators. These twenty separateexperiments were conducted in random order within operators.

In this Gage R&R study, the following method conditions were fixed:

-   -   Photosensitive Entity: Riboflavin dissolved in pH 6.4 aqueous        phosphate buffer solution at a target concentration of 30.5±1.5        mg/L    -   Temperature: 4±1° C.    -   Atmosphere: Air    -   Light Power Density: 0.375±0.005 W/cm² as measured at the holder        31 monitoring position that is located along light path 33 and        is 6 inches from shutter 26 and 4 inches from iris 28    -   Sampling Times: samples withdrawn after 0, 10, 40, 80, 120, 160,        and 200 minutes of light exposure    -   Photosensitive Entity Analysis Method: HPLC analysis of removed        sample aliquots for their riboflavin concentration

The test materials used for the study consisted of titanium dioxidepigmented low density polyethylene (LDPE) films prepared by cast filmextrusion of approximately 48 μm thickness. Film swatches of about 6 cmby 13 cm were cut from a larger film sample for study. Each swatch wasevaluated with light exposure only once. After evaluation, each swatchwas set aside for additional measurements including replicate measuresof TAPPI opacity and film thickness where average values are reported.

The output of each run was the pseudo-first order rate constant ofriboflavin decomposition. The decomposition of riboflavin in diluteaqueous solution under ambient atmosphere (i.e., excess of O₂ availablefor reaction as a dissolved gas in the solution) has been shown tofollow pseudo-first order rate kinetics when said solution is exposed toUV or visible light (e.g., Ahmad, I.; Fasihullah, Q.; Noor, A; Ansari,I. A.; Ali, Q. Nawab Manzar, International Journal of Pharmaceutics(2004), 280(1-2), 199-208). More specifically, under conditions duringwhich the energy distribution of light that is incident upon saidsolution is held constant, said decomposition can be described by thefollowing integrated rate expression:Ln [Riboflavin]_(t)=(−k′×t)+Ln [Riboflavin]₀  Eq. 1

-   -   where:        -   [Riboflavin]_(t)=Riboflavin concentration at time=t        -   [Riboflavin]₀=Initial riboflavin concentration prior to            light exposure        -   t=Light exposure time        -   k′=Pseudo-first order rate constant

When such reaction kinetics are observed, a plot of Ln [Riboflavin]_(t)versus exposure time yields a straight line, the slope of which is thedesired pseudo-first order rate constant. For this work, the rateconstant plot was constructed using Minitab software, and the desiredslope data was extracted using the Minitab ‘Fitted Line Plot’ analysistool. The correlation coefficient (R²) values indicating the quality ofthe linear fit derived from the linear regression analyses were all98.5% or greater, showing excellent agreement of the data to the linearmodel. A sample of these data is shown in FIG. 5 and the complete set ofreduced data from each of the plots generated as such is presented inTable 1.

TABLE 1 Gage R&R Data Rate Average Run Test Constant TAPPI OpacityAverage Film Order Material Operator (min⁻¹) (%) Thickness (in) 1 O1 10.0180 28.04 0.00188 2 O4 1 0.0029 66.47 0.00185 3 O5 1 0.0018 78.820.00189 4 O2 1 0.0087 39.87 0.00186 5 O3 1 0.0047 52.88 0.00183 6 O1 20.0195 28.17 0.00184 7 O5 2 0.0016 78.71 0.00204 8 O4 2 0.0029 66.280.00190 9 O3 2 0.0045 52.17 0.00188 10 O2 2 0.0084 39.88 0.00190 11 O1 10.0199 28.22 0.00188 12 O5 1 0.0020 79.11 0.00196 13 O4 1 0.0035 65.550.00184 14 O2 1 0.0098 39.20 0.00183 15 O3 1 0.0047 53.10 0.00193 16 O12 0.0196 28.42 0.00189 17 O3 2 0.0050 52.77 0.00190 18 O5 2 0.0019 79.200.00193 19 O4 2 0.0031 66.02 0.00193 20 O2 2 0.0084 40.08 0.00191

The Gage R&R ANOVA analysis was conducted on the data presented inTable 1. The analysis outputs are shown in Table 2. This analysisrevealed that 0.58% of the total contribution and 7.6% of the totalstudy variability can be attributed to the measurement system with theremainder due to inherent (and by design) part-to-part variations. Witha 95% confidence level for the data interpretation, neitheroperator-to-operator differences nor operator-part interactions wereobserved in this study (Operator ANOVA ‘p’ value=0.661 withoperator-part interactions included; Operator*part ANOVA ‘p’value=0.445). Thus, this study demonstrated that the measurement systemis capable of producing reproducible and repeatable data.

TABLE 2 Minitab Analysis Outputs of the ANOVA Analysis of the Gage R&RStudy % Contribution Source VarComp (of VarComp) Total Gage R&R0.0000003 0.58 Repeatability 0.0000003 0.58 Reproducibility 0.00000000.00 Operators 0.0000000 0.00 Part-To-Part 0.0000496 99.42 TotalVariation 0.0000499 100.00 Study Var % Study Var Source StdDev (SD) (6 *SD) (% SV) Total Gage R&R 0.0005382 0.0032292 7.62 Repeatability0.0005382 0.0032292 7.62 Reproducibility 0.0000000 0.0000000 0.00Operators 0.0000000 0.0000000 0.00 Part-To-Part 0.0070455 0.042272799.71 Total Variation 0.0070660 0.0423959 100.00 Number of DistinctCategories = 18

The Gage R&R results thus suggest that the measurement system will yieldriboflavin degradation rate constant data of adequate quality. Thisconclusion can be reasonably extended to other riboflavin based exposurestudies that involve reasonable deviations away from the exposureconditions utilized in this Gage R&R study. Examples of such deviationsinclude modifications to the following: the light power density and/orspectral distribution that are incident upon the light attenuating testmaterial, the riboflavin solution temperature, the initial riboflavinsolution concentration and the test material composition. Additionalextensions include other (i.e., non-riboflavin) photosensitive entitiesthat may or may not exhibit pseudo-first order light-induced change ordegradation rate kinetics and that may or may not be dissolved in water.

Example 2

The rate constant data from Example 1 are analyzed along with additionalcharacterization of the film swatches. The rate constant data in Table 1are plotted in FIG. 6 along with a fitted data model. This data modelwas fitted to the data points to yield a photoprotective model as afunction of TAPPI Opacity.

This plot and model demonstrate that the light protection performance(i.e., the ability to protect riboflavin from light-induced degradation)of the titanium dioxide loaded LDPE films utilized in this study canindeed be linked to the TAPPI Opacity values of said films, arelationship that is well described by a power law model. Thisdemonstrates that for this type of material that TAPPI Opacity can beused to predict light protection performance. This method could beapplied to other materials.

These type of data can in turn be used in the method of the presentinvention to confidently predict the light protection performance fortest material comprised of the same or comparable titanium dioxide andLDPE materials in different relative amounts with a known TAPPI opacity.

Example 3

The same method conditions were utilized as in Example 1, but theevaluated samples were two pieces (each evaluated in duplicate) derivedfrom the same titanium dioxide pigmented, high density polyethylene(HDPE) bottle (1.8 wt % titanium dioxide, 1380 μm wall thickness, 97.9%TAPPI Opacity) and HPLC analysis was performed after 0, 10, 25, 50, 75,100, and 125 minutes of light exposure. The R² values obtained from thelinear regression analyses were all 98.5% or greater and a meanpseudo-first order rate constant of 0.000691 min⁻¹ for riboflavindecomposition was found.

Example 4

The same method conditions were utilized as in Example 3, but theevaluated samples consisted of four film swatches (each evaluated once)derived from the same piece of titanium dioxide pigmented, low densitypolyethylene film (1.0 wt % titanium dioxide, 47 μm thick, 39% TAPPIOpacity) and two of the swatches were evaluated at 14±1° C. The R²values obtained from the linear regression analyses were all 99.3% orgreater and the 4° C. and 14° C. mean pseudo-first order rate constantswere found to be 0.0112 min⁻¹ and 0.0154 min⁻¹, respectively, indicatingthe sensitivity of temperature on the riboflavin degradation kinetics asconsistent with the theoretical predictions given by the Arrheniusequation.

Example 5

The same method conditions were utilized as in Example 3, but theevaluated samples consisted of four film swatches (each evaluated once)derived from the same piece of titanium dioxide pigmented, low densitypolyethylene film (1.0 wt % titanium dioxide, 47 μm thick, 40% TAPPIOpacity) and two of the swatches were evaluated using a UV light filterattenuated light beam (essentially complete wavelength cutoff at 385 nmand below), said beam possessing a light power density of 0.287 W/cm².The R² values obtained from the linear regression analyses were all99.3% or greater and the mean pseudo-first order rate constants with andwithout the UV light attenuating filter in the light beam were found tobe 0.0062 min⁻¹ and 0.0112 min⁻¹, respectively, indicating thesensitivity of riboflavin degradation kinetics to the presence of UVlight.

Example 6

The same method conditions were utilized as in Example 3, but theevaluated samples consisted of four film swatches (each evaluated once)derived from the same piece of titanium dioxide pigmented, low densitypolyethylene film (1.0 wt % titanium dioxide, 47 μm thick, 40% TAPPIOpacity) and two of said swatches were evaluated using a reduced lightpower density of 0.201 W/cm². The R² values obtained from the linearregression analyses were all 99.3% or greater and the mean pseudo-firstorder rate constants for light power densities of 0.375 W/cm² and 0.201W/cm² were found to be 0.0112 min⁻¹ and 0.0045 min⁻¹, respectively,indicating the sensitivity of riboflavin degradation kinetics to lightpower density.

Example 7

The same method conditions were utilized as in Example 3, but theevaluated samples consisted of four film swatches (each evaluated once)derived from the same piece of titanium dioxide pigmented, low densitypolyethylene film (2.0 wt % titanium dioxide, 50 μm thick, 53% TAPPIOpacity) and two of the swatches were evaluated under an atmosphere ofcarbon dioxide (<0.2 ppm oxygen in the test solution after 15 minutes ofCO2 sparging; oxygen levels in test solution were measured with an OceanOptics—Dunedin, Fla.—NeoFox oxygen sensor). The R2 values obtained fromthe linear regression analyses were all 98.4% or greater. The airatmosphere and carbon dioxide atmosphere mean pseudo-first order rateconstants were found to be 0.0057 min-1 and 0.0172 min-1, respectively,indicating the sensitivity of the light induced degradation kinetics ofriboflavin to the atmosphere present in the sample cell.

Example 8

The same method conditions were utilized as in Example 3, but theevaluated samples consisted of two film swatches (each evaluated once)derived from the same piece of titanium dioxide pigmented, low densitypolyethylene film (2.0 wt % titanium dioxide, 47 μm thick, 53% TAPPIOpacity), the initial riboflavin concentration was 15 mg/L, the lightintensity was 0.600 W/cm2 at the film swatch position and HPLC analysisof the sample cell solution was replaced by an in-situ UV-VISspectroscopic analysis (riboflavin absorbance data at 447 nm capturedevery minute for 60 minutes) using a fiber dip probe (Model #FDP-200-0.22-1.5-S; B&W Tek, Inc., Newark, Del.) inserted into saidsolution for the duration of the light exposure experiment. Rateconstant data were calculated as indicated in Example 1 substitutingabsorbance values for concentration values in Equation 1. The R2 valuesobtained from the linear regression analyses were all 99.7% or greater.A mean pseudo-first order rate constant of 0.0050 min-1 was foundindicating that the generation of degradation rate data for a lightexposed, photosensitive entity can be obtained without the need toremove material from the sample cell.

Example 9

The light protection performance of an untested material of known TAPPIopacity within the same class of materials of that used to construct thephotoprotective model of Example 2 (FIG. 6) is predicted using saidmodel. The TAPPI opacity of the untested material was measured to be35.95%. The model predicts a k′ value 0.0112 min⁻¹. The k′ value of theuntested material was measured to be 0.0111 min⁻¹ using the method ofExample 1 with the HPLC analyses performed after 0, 10, 25, 50, 75, 100,and 125 minutes of light exposure. These results demonstrate that thephotoprotective model is useful for the prediction of light protectionperformance based upon TAPPI opacity.

Example 10

Each of three injection molded pigmentary titanium dioxide filled, highdensity polyethylene (HDPE) bottles (bottle dimensions: 7.16″ high×3.29″wide×2.1″ deep; total bottle internal volume: 528 mL), said bottlesdifferentiated by TAPPI opacity value (achieved by adjusting theirtitanium dioxide loading), were each cut apart to yield one flat,rectangularly shaped plaque per bottle (plaque dimensions: ˜10.9 cm longby ˜6.4 cm wide by ˜1232 microns thick). A fourth bottle of the samedimensions and composition as the first three but unpigmented wassimilarly cut apart and the resulting plaque then completely covered onone side by a thin (˜15 microns) sheet of aluminum foil. The fourplaques, coded X, Y, Z and F (the latter code denoting the foil wrapped,unpigmented plaque), were evaluated in duplicate for their lightprotection properties using the method conditions described in Example 1with the exception that the samples for HPLC analyses were withdrawn at0, 10, 25, 50, 75, 100 and 125 minutes of light exposure and the lightpower density utilized was 0.425±0.005 W/cm². The average pseudo-firstorder rate constant (k′) value for riboflavin degradation that wasobtained for each of the plaques is shown in Table 3.

TABLE 3 Average Pseudo-First Oder Rate Constant (k′) Values Plaque CodeTAPPI Opacity (%) k′ (day⁻¹) X 81.4 3.19 Y 92.4 1.70 Z 99.2 0.60 F 99.70.28

Example 11

Multiple intact bottles of the same dimensions and compositions as thefour studied as excised plaques in Example 10 were sanitized with anaqueous solution of bleach (100 ppm chlorine), thoroughly rinsed withdeionized/distilled water and drained. Milk products, specifically 2%milk and 2% milk fortified with omega-3 fatty acids, were separatelypackaged into said bottles shortly after standard beverage processingwith efforts being made to avoid light exposure. The filled bottles werethen sealed with sanitized caps. A complete aluminum foil wrap was thenapplied to each of the filled, unpigmented HDPE bottles. Coding of thefilled bottles mirrored that utilized in Example 10. This activityyielded eight separate sample bottle groups each one representing adifferent bottle code/milk product combination (four different bottlecodes×2 different milk products). Said groups were then simultaneouslyand randomly placed into a single walk-in, lighted, chilled dairy case(Friedrich Floating Air beverage case, Model #60-10-1056, San Antonio,Tex.) for a 5 week long, simulated retail storage exposure study. Duringsaid study, the light intensity (sampled at three different interiorlocations) and temperature inside said case were found to be 1122±439lux and 2.7±0.8° C., respectively. Depending on sampling location, thelight intensity in the interior of the case ranged from a low of 355 luxto a high of 1942 lux. At pre-determined times, two bottles wererandomly selected from each of the eight sample bottle groups andremoved from the dairy case. The contents of said bottles were thenanalyzed in triplicate in a spectrofluorometer (Shimadzo ScientificInstrument, Inc., Columbia, Md.) for riboflavin concentration whilefollowing a modification of the Association of Analytical Communities(AOAC) method 960.65 (See also Webster, J B, Duncan, S E, Marcy, J E,O'Keefe, S F. 2009. Controlling light oxidation flavor in milk byblocking riboflavin excitation wavelengths by interference. J. Food Sci.74:390-398; Bradley, R L. 2000. Dairy Products. In: William Horwitz W,editor. Official Methods of Analysis of AOAC Intl. Vol. H. 17th ed.Gaithersburg, Md.: AOAC Int. 1-83). The obtained riboflavinconcentration versus light exposure time data is reported in Table 4.

TABLE 4 Riboflavin Concentrations of Milk Products in Dairy Case StorageRiboflavin Days of Bottle Concentration Storage Milk Product Code(ug/mL) 0 2% Milk F 23.92 0 2% Milk F 24.51 1 2% Milk F 23.72 1 2% MilkF 23.62 3 2% Milk F 24.00 3 2% Milk F 25.20 8 2% Milk F 24.60 8 2% MilkF 23.60 15 2% Milk F 21.24 15 2% Milk F 22.17 22 2% Milk F 22.83 22 2%Milk F 22.61 29 2% Milk F 23.30 29 2% Milk F 22.91 36 2% Milk F 22.50 362% Milk F 21.80 0 2% Milk X 23.92 0 2% Milk X 24.51 1 2% Milk X 23.46 12% Milk X 23.57 3 2% Milk X 21.30 3 2% Milk X 21.00 8 2% Milk X 16.80 82% Milk X 18.30 15 2% Milk X 15.23 15 2% Milk X 13.85 22 2% Milk X 12.9622 2% Milk X 13.16 29 2% Milk X 14.28 29 2% Milk X 14.37 36 2% Milk X5.85 36 2% Milk X 7.58 0 2% Milk Y 23.92 0 2% Milk Y 24.51 1 2% Milk Y23.44 1 2% Milk Y 23.40 3 2% Milk Y 23.80 3 2% Milk Y 22.50 8 2% Milk Y20.40 8 2% Milk Y 19.10 15 2% Milk Y 16.75 15 2% Milk Y 20.75 22 2% MilkY 20.13 22 2% Milk Y 16.07 29 2% Milk Y 16.59 29 2% Milk Y 16.37 36 2%Milk Y 18.02 36 2% Milk Y 18.16 0 2% Milk Z 23.92 0 2% Milk Z 24.51 1 2%Milk Z 23.67 1 2% Milk Z 22.80 3 2% Milk Z 23.20 3 2% Milk Z 24.20 8 2%Milk Z 19.00 8 2% Milk Z 21.30 15 2% Milk Z 20.53 15 2% Milk Z 21.37 222% Milk Z 22.55 22 2% Milk Z 22.15 29 2% Milk Z 21.69 29 2% Milk Z 21.2736 2% Milk Z 13.72 36 2% Milk Z 18.70 0 2% Milk with Omega-3 F 23.75 02% Milk with Omega-3 F 23.67 1 2% Milk with Omega-3 F 23.09 1 2% Milkwith Omega-3 F 23.27 3 2% Milk with Omega-3 F 22.85 3 2% Milk withOmega-3 F 22.71 8 2% Milk with Omega-3 F 20.78 8 2% Milk with Omega-3 F19.90 15 2% Milk with Omega-3 F 21.18 15 2% Milk with Omega-3 F 21.48 222% Milk with Omega-3 F 21.04 22 2% Milk with Omega-3 F 20.90 29 2% Milkwith Omega-3 F 18.28 29 2% Milk with Omega-3 F 17.94 36 2% Milk withOmega-3 F 17.61 36 2% Milk with Omega-3 F 14.88 0 2% Milk with Omega-3 X23.75 0 2% Milk with Omega-3 X 23.67 1 2% Milk with Omega-3 X 20.97 1 2%Milk with Omega-3 X 20.98 3 2% Milk with Omega-3 X 14.53 3 2% Milk withOmega-3 X 14.03 8 2% Milk with Omega-3 X 11.43 8 2% Milk with Omega-3 X10.99 15 2% Milk with Omega-3 X 6.27 15 2% Milk with Omega-3 X 6.43 222% Milk with Omega-3 X 6.61 22 2% Milk with Omega-3 X 5.80 29 2% Milkwith Omega-3 X 5.23 29 2% Milk with Omega-3 X 5.34 36 2% Milk withOmega-3 X 5.58 36 2% Milk with Omega-3 X 4.97 0 2% Milk with Omega-3 Y23.75 0 2% Milk with Omega-3 Y 23.67 1 2% Milk with Omega-3 Y 21.05 1 2%Milk with Omega-3 Y 21.09 3 2% Milk with Omega-3 Y 18.77 3 2% Milk withOmega-3 Y 19.04 8 2% Milk with Omega-3 Y 16.33 8 2% Milk with Omega-3 Y18.88 15 2% Milk with Omega-3 Y 10.49 15 2% Milk with Omega-3 Y 14.51 222% Milk with Omega-3 Y 9.53 22 2% Milk with Omega-3 Y 9.65 29 2% Milkwith Omega-3 Y 11.75 29 2% Milk with Omega-3 Y 11.87 36 2% Milk withOmega-3 Y 8.16 36 2% Milk with Omega-3 Y 9.80 0 2% Milk with Omega-3 Z23.75 0 2% Milk with Omega-3 Z 23.67 1 2% Milk with Omega-3 Z 21.49 1 2%Milk with Omega-3 Z 21.19 3 2% Milk with Omega-3 Z 20.88 3 2% Milk withOmega-3 Z 21.39 8 2% Milk with Omega-3 Z 18.79 8 2% Milk with Omega-3 Z19.05 15 2% Milk with Omega-3 Z 16.69 15 2% Milk with Omega-3 Z 15.86 222% Milk with Omega-3 Z 15.84 22 2% Milk with Omega-3 Z 14.93 29 2% Milkwith Omega-3 Z 16.07 29 2% Milk with Omega-3 Z 13.87 36 2% Milk withOmega-3 Z 13.70 36 2% Milk with Omega-3 Z 13.85

Utilizing the integrated rate expression provided in Example 1, thepseudo-first order rate constant (k′) of riboflavin degradation wascalculated from the data in Table 4 for each bottle code/milk productcombination. The R² values obtained from the linear regression analysesranged from 46% to 91%, values which are notably lower than thoseobtained from the method of Example 10 most likely due to the complexityof the milk product and the steps required to prepare the milk samplefor analysis. The resulting k′ and R² values are provided in Table 5.

TABLE 5 Riboflavin k′ for Milk Products Under Retail Storage Conditions.Bottle Milk Product Code k′ (day⁻¹) R² (%) 2% Milk X 0.0286 84.4 2% MilkY 0.0095 67.4 2% Milk Z 0.0074 46.4 2% Milk F 0.0024 44.7 2% Milk withOmega-3 X 0.0419 84.3 2% Milk with Omega-3 Y 0.0255 84.0 2% Milk withOmega-3 Z 0.0140 90.6 2% Milk with Omega-3 F 0.0090 81.2

The k′ data contained within Table 5 of the current Example was comparedto those contained within Table 3 of Example 10. Said comparisonrevealed a linear relationship between the two sets of data for bothmilk products (2% milk product, R²=92%; 2% milk product with omega-3,R²=100%).

Thus, it can be seen that the accelerated light exposure testing methoddescribed in Example 10, which makes use of a simple aqueous riboflavinsolution and takes only a few hours per sample to complete, can beadvantageously used in lieu of the much more involved light exposuretesting method described in the current Example to predict the long term(30+ days) light exposure behavior of a riboflavin containing, complexfood system (milk) that is kept in a real world packaging environment(HDPE bottle) under real world storage conditions (lighted, refrigeratedstorage case).

Example 12

At pre-determined times during the long term exposure study described inExample 11, for each of the eight sample bottle groups, six bottles weresimultaneously and randomly removed from the storage case and theircontents combined to form a homogenous mixture. Approximately 3 hourslater, the resulting eight mixtures (which were stored until use inone-fluid ounce portions in ice filled coolers that were kept in a 4°C., walk-in refrigerator) were presented to a group of human subjects(individual appraisers) for evaluation of their sensory properties usingstandard triangle testing methodology (e.g., ASTM E1885-04, StandardTest Method for Sensory Analysis—Triangle Test). Also evaluated at thesame light exposure time points were mixtures derived from twoadditional sample bottle groups. These latter two groups were producedand stored as described in Example 11 (one group was associated with the2% milk product and the other with the 2% milk product fortified withomega-3 fatty acids) but made use of unpigmented, HOPE bottles that werenot wrapped in aluminum foil (40.7% average TAPPI opacity, bottle code‘C’). Table 6 provides the bottle code paired comparisons that werestudied for each milk product along with the storage times at which thesensory evaluations were performed and the resulting sensory evaluationresponse data (total responses and correct responses). Also provided inTable 6 are associated ‘Correct Response Proportion’ values, denoted inthis study as R, which were calculated by dividing the number of correctappraiser responses by the total number of appraiser responses.

TABLE 6 Aggregated Sensory Evaluation Data Appraiser Responses CorrectBottle Code Storage Response Paired Time Proportion Milk ProductComparison (days) Total (#) Correct (#) Values, R 2% Milk with Omega-3F:C 1 25 15 0.60 2% Milk with Omega-3 F:X 1 25 12 0.48 2% Milk withOmega-3 F:Y 1 24 13 0.54 2% Milk with Omega-3 F:Z 1 24 17 0.71 2% Milkwith Omega-3 F:C 3 28 18 0.64 2% Milk with Omega-3 F:X 3 27 10 0.37 2%Milk with Omega-3 F:Y 3 27 12 0.44 2% Milk with Omega-3 F:Z 3 26 8 0.312% Milk with Omega-3 F:C 7 34 24 0.71 2% Milk with Omega-3 F:X 7 35 210.60 2% Milk with Omega-3 F:Y 7 36 19 0.53 2% Milk with Omega-3 F:Z 7 3611 0.31 2% Milk with Omega-3 F:C 14 39 34 0.87 2% Milk with Omega-3 F:X14 38 21 0.55 2% Milk with Omega-3 F:Y 14 36 22 0.61 2% Milk withOmega-3 F:Z 14 38 21 0.55 2% Milk with Omega-3 F:C 21 35 29 0.83 2% Milkwith Omega-3 F:X 21 35 30 0.86 2% Milk with Omega-3 F:Y 21 34 30 0.88 2%Milk with Omega-3 F:Z 21 34 15 0.44 2% Milk with Omega-3 F:C 28 35 280.80 2% Milk with Omega-3 F:X 28 35 30 0.86 2% Milk with Omega-3 F:Y 2835 30 0.86 2% Milk with Omega-3 F:Z 28 34 20 0.59 2% Milk F:C 1 28 110.39 2% Milk F:X 1 28 13 0.46 2% Milk F:Y 1 26 12 0.46 2% Milk F:Z 1 257 0.28 2% Milk F:C 3 28 19 0.68 2% Milk F:X 3 27 13 0.48 2% Milk F:Y 331 18 0.58 2% Milk F:Z 3 29 12 0.41 2% Milk F:C 8 35 28 0.80 2% Milk F:X8 34 15 0.44 2% Milk F:Y 8 34 18 0.53 2% Milk F:Z 8 35 19 0.54 2% MilkF:C 15 39 30 0.77 2% Milk F:X 15 38 31 0.82 2% Milk F:Y 15 38 20 0.53 2%Milk F:Z 15 38 16 0.42 2% Milk F:C 22 36 30 0.83 2% Milk F:X 22 36 260.72 2% Milk F:Y 22 36 22 0.61 2% Milk F:Z 22 35 14 0.40 2% Milk F:C 2937 29 0.78 2% Milk F:X 29 38 33 0.87 2% Milk F:Y 29 38 30 0.79 2% MilkF:Z 29 38 17 0.45

Modeling of sensory data can provide a useful means to estimateparameters such as the sensory shelf-life of a food system (e.g., Houghand Garitta (G. Hough, L. Garitta, Journal of Sensory Studies, 27 (2012)137-147).

Inspection of Table 6 reveals a time dependence for the calculated Rvalues, denoted as R(t). The R(t) data was modeled (non-linearregression) for all of the milk product/bottle code paired comparisoncombinations that were evaluated using the logistic growth equationR(t)=A+(B−A)/{1+exp[(t−C)/D]}, where t represents exposure time and A,B, C and D are constants which can be characterized within the contextof this triangle test study as follows:

A: This constant represents the fraction of appraisers who can correctlydiscriminate among the different sample bottle codes for a given milkproduct at extended exposure times. It should be noted that not allappraisers are capable of providing such discrimination, and, as such,the value of this constant is typically below its theoretical maximum of1.00. The value of this constant is generally influenced by thepopulation of appraisers and the product being evaluated for its sensoryproperties.

B: This constant represents the fraction of appraisers who can correctlydiscriminate among the different sample bottle codes for a given milkproduct at early exposure times. It should be noted that during theearly stages of an exposure study such as this one, differences amongthe sample bottle groups being evaluated are usually not evident exceptto the most highly discriminating appraisers. As such, this constant(using triangle test methodology) is typically not far from the 0.33value associated with chance guessing. The value of this constant isalso generally influenced by the population of appraisers and theproduct being evaluated for its sensory properties.

C: This constant, the value of which is generally influenced by packagetype (milk bathe in the case of this study) as well as by product typeand package storage time and conditions, represents the exposure time atwhich the R(t) function inflects or, put differently, transitions fromthe exposure time discrimination region defined by constant B (earlyexposure times) to that defined by constant A (extended exposure times).For the purposes of this study, we denote said time (i.e. C) as aSensory Shelf Life (SSL) and use it as the metric by which the lightprotection performances of the sample bottles evaluated in this studyare quantified. Note that increasing SSL values imply increasing lightprotection capability for a particular bottle type or, put differently,imply that longer sample bottle storage times are required before amajority of the appraisers participating in this study can correctlydistinguish the sensory difference between a given experimental bottleand that of an aluminum foil wrapped control bottle.

D: This constant provides a measure of how sharp the transition isthrough the R(t) function inflection point. The value of this constantis also generally influenced by package type (milk bottle in the case ofthis study) as well as package storage time and conditions.

Table 7 lists the SSL data derived from the logistic growth functionmodeling of the time dependent ‘Correct Response Proportion’ data givenin Table 6.

TABLE 7 Sensory Shelf Life (SSL) Data Bottle Codes for Paired MilkProduct Comparison SSL (days) 2% Milk F:C  3 F:X 13 F:Y 25 F:Z  30+* 2%Milk with F:C  1 Omega-3 F:X  7 F:Y  9 F:Z 28 Notes: *Inspection of thedata from this milk product/paired comparison combination showed thatthe transition to the extended exposure time discrimination regionoccurred at a light exposure time point that was greater than the 30days of the exposure study.

The X, Y, and Z plaque derived k′ data contained within Table 3 ofExample 10 were converted to their corresponding half-lives (t½=ln2/k′). These were compared to the corresponding SSL data containedwithin Table 7 of the current Example (plaque

paired comparison: X

F:X, Y

F:Y and Z

F:Z). Said comparison revealed a linear relationship between the twosets of data for both milk products (2% milk product, R²=100%; 2% milkproduct with omega-3, R²=96%).

Thus, it can be seen that the accelerated light exposure testing methoddescribed in Example 10, which makes use of a simple aqueous riboflavinsolution and takes only a few hours per sample to complete, can beadvantageously used in lieu of the much more involved light exposuretesting method described in the current Example to predict the long term(30+ days) sensory performance behavior of a riboflavin containing,complex food system (milk) that is kept in a real world packagingenvironment (HDPE bottle) under real world storage conditions (lighted,refrigerated storage case).

What is claimed is:
 1. A method for producing a packaging designcomprising: (a) providing a known homogeneous liquid sample comprisingone or more known photosensitive entities at appropriate concentrations;(b) providing a cell having controlled optical properties to contain thesample at a controlled temperature between about −20° C. and about 100°C.; (c) providing a light source that generates a light beam with acontrolled spectral signature between about 290 and about 1000 nm and acontrolled intensity between about 0.01 and about 5 W/cm²; (d) placingthe sample into the cell, rendering a sample cell; (e) placing a firsttest material between the light source and the sample cell, rendering ashielded sample cell, wherein the light beam impinges upon the firsttest material and any penetrating light impinges upon the sample cell,and wherein the first test material comprises a known quantitative orqualitative property; (f) exposing the shielded sample cell to one ormore light beam intensities for one or more durations; (g) measuringchanges to the one or more photosensitive entities contained within theshielded sample cell at one or more durations to generate data points;(h) using the data points to determine a photoprotective performancevalue of the first test material; (i) repeating steps (a)-(h) with oneor more additional test materials under the same conditions to generateone or more additional photoprotective performance values; (j) utilizingthe two or more photoprotective performance values to generate a modelfor a class of materials that relates the known qualitative orquantitative property to the photoprotective performance; (k) using themodel of step (j) to identify a photoprotective performance value of apackaging material; and (l) using the packaging material in step (k) todesign a package.
 2. The method of claim 1, wherein the knownhomogeneous liquid sample comprises an aqueous solution of riboflavin.3. A method for predicting the photoprotective performance of amaterial, the method comprising: (a) providing a known homogeneousliquid sample comprising one or more known photosensitive entities atappropriate concentrations; (b) providing a cell having controlledoptical properties to contain the sample at a controlled temperaturebetween about −20° C. and about 100° C.; (c) providing a light sourcethat generates a light beam with a controlled spectral signature betweenabout 290 and about 1000 nm and a controlled intensity between about0.01 and about 5 W/cm²; (d) placing the sample into the cell, renderinga sample cell; (e) placing a first test material between the lightsource and the sample cell, rendering a shielded sample cell, whereinthe light beam impinges upon the first test material and any penetratinglight impinges upon the sample cell, and wherein the first test materialcomprises a known quantitative or qualitative property; (f) exposing theshielded sample cell to one or more light beam intensities for one ormore durations; (g) measuring changes to the one or more photosensitiveentities contained within the shielded sample cell at one or moredurations to generate data points; (h) using the data points todetermine a photoprotective performance value of the first testmaterial; (i) repeating steps (a)-(h) with one or more additional testmaterials under the same conditions to generate one or more additionalphotoprotective performance values; (j) utilizing the two or morephotoprotective performance values to generate a model for a class ofmaterials that relates the known qualitative or quantitative property tothe photoprotective performance; and (k) using the model of step (j),for materials of the same class, predicting an unknown qualitative orquantitative property of a material based upon a known photoprotectiveperformance value or predicting an unknown photoprotective performancevalue based upon a known qualitative or quantitative property of thematerial.
 4. The method of claim 3 further comprising the step (l) ofrepeating at least one of steps (h) and (j) at least one time to createa library of data which includes the photoprotective performance valuesas well one or more values describing the qualitative or quantitativeproperties of the material.
 5. The method of claim 4 further comprisingthe step: (m) optionally using the library from step (l) to identify,describe, and/or predict properties of materials.
 6. The method of claim3, wherein the models is used to facilitate the design of packages thatprovide targeted photoprotection appropriate for contents in thepackage.
 7. The method of claim 6, wherein the package contents includefood, beverages, drugs, pharmaceuticals, and/or other photosensitiveproducts.
 8. The method of claim 3, wherein the models are used toassess sustainability metrics for a package design.
 9. The method ofclaim 3, wherein the method further comprises correlating the changes tothe one or more photosensitive entities in step (g) with one or moresensory evaluation criteria values and utilizing said predictedphotoprotective performance to further predict one or more sensoryevaluation criteria values of an untested material.
 10. The method ofclaim 3, wherein the sample is maintained under controlled atmosphereconditions.
 11. The method of claim 3, wherein the measuring of step (g)comprises evaluating the changes to the one or more photosensitiveentities while the sample is contained within the cell.
 12. The methodof claim 3, wherein the measuring of step (g) comprises removing asample from the cell and evaluating the changes to the one or morephotosensitive entities externally.
 13. The method of claim 3, whereinthe one or more photosensitive entities are constituents of food,beverages, drugs, pharmaceuticals, or other photosensitive products. 14.The method of claim 3, wherein said sample comprises one or morephotosensitive entities selected from one or more of the followingclasses: i. natural and synthetic food additives, dyes, and pigments;ii. chlorophyll; iii. myoglobin, oxymyoglobin, and other hemeproteins;iv. water and fat soluble essential nutrients, minerals, and vitamins;v. food components containing fatty acids; vi. oils; vii. proteins;viii. pharmaceutical compounds; ix. personal care and cosmeticformulation compounds; x. household chemicals and their components; andxi. agricultural chemicals and their components.
 15. The method of claim14, wherein said sample comprises one or more photosensitive entitiesselected from two or more of the classes.
 16. The method of claim 14,wherein said sample comprises one or more photosensitive entitiesselected from three or more of the classes.
 17. The method of claim 14,wherein said sample comprises one or more photosensitive entitiesselected from four or more of the classes.
 18. The method of claim 14,wherein said sample comprises one or more photosensitive entitiesselected from five or more of the classes.
 19. The method of claim 14,wherein said sample comprises one or more photosensitive entitiesselected from six or more of the classes.
 20. The method of claim 14,wherein said sample comprises one or more photosensitive entitiesselected from seven or more of the classes.
 21. The method of claim 14,wherein said sample comprises one or more photosensitive entitiesselected from eight or more of the classes.
 22. The method of claim 14wherein said sample comprises one or more photosensitive entitiesselected from nine or more of the classes.
 23. The method of claim 14,wherein said sample comprises one or more photosensitive entitiesselected from ten or more of the classes.
 24. The method of claim 3,wherein said measuring comprises a test method selected from the groupconsisting of HPLC, GC, IR spectroscopy, NMR spectroscopy, UV-VISspectroscopy, GC-MS, LC-MS, fluorescence spectroscopy, ionchromatography, thin layer chromatography, analytical wet chemistry, andelectrochemical analysis.
 25. A method for predicting thephotoprotective performance of a package material for a liquid milkproduct, the method comprising: (a) providing a sample comprising anaqueous solution of riboflavin at about 30 ppm and test materialsderived from a series of high density polyethylene packages withdiffering loadings of titanium dioxide; (b) utilizing the method ofclaim 3 to generate riboflavin degradation rate constant data values foreach test material; (c) exposing a liquid milk product in test packagesconstructed of the test materials described in (a) to retail storageconditions and measuring riboflavin content data of said milk productfor each test package as a function of exposure time; (d) reducing thedata from (c) to a riboflavin degradation rate constant value for eachtest package; (e) relating the rate constant values from step (b) to therate constant values from step (d) to develop a functional correlation;(f) testing a packaging test material comprised of high densitypolyethylene and TiO2 using steps (a) and (b) to generate the rateconstant data value and using the rate constant data value and thefunctional correlation of step (e) to predict the photoprotectiveperformance under retail storage conditions of a package comprised ofthe test material and containing the given liquid milk product.
 26. Themethod of claim 25 where time required to obtain predictivephotoprotective performance data of a package under retail storageconditions is accelerated by at least 100 times.
 27. The method of claim3, wherein the known homogeneous liquid sample comprises an aqueoussolution of riboflavin.
 28. A method for predicting the photoprotectiveperformance of a material, the method comprising: (a) providing a knownhomogeneous liquid sample comprising one or more known photosensitiveentities at appropriate concentrations; (b) providing a cell havingcontrolled optical properties to contain the sample at a controlledtemperature between about −20° C. and about 100° C.; (c) providing alight source that generates a light beam with a controlled spectralsignature between about 290 and about 1000 nm and a controlled intensitybetween about 0.01 and about 5 W/cm²; (d) placing the sample into thecell, rendering a sample cell; (e) placing a first test material betweenthe light source and the sample cell, rendering a shielded sample cell,wherein the light beam impinges upon the first test material and anypenetrating light impinges upon the sample cell, and wherein the firsttest material comprises a known quantitative or qualitative property;(f) exposing the shielded sample cell to one or more light beamintensities for one or more durations; (g) measuring changes to the oneor more photosensitive entities contained within the shielded samplecell at one or more durations to generate data points; (h) using thedata points to determine a photoprotective performance value of thefirst test material; (i) repeating steps (a)-(h) with one or moreadditional test materials under the same conditions to generate one ormore additional photoprotective performance values; (j) utilizing thetwo or more photoprotective performance values to generate a model for aclass of materials that relates the known qualitative or quantitativeproperty to the photoprotective performance; and (k) using the model ofstep (j), to identify a photoprotective performance value of a packagingmaterial.